A melting and casting batching method and system based on mixed materials

Abstract

The present invention is applicable to the field of alloy smelting technology, and provides a melting and casting batching method and system based on mixed materials. The method includes constructing an objective function based on the product quality of the mixed materials, the number of storage areas for the marked mixed materials, and the cost of the mixed materials; determining that the input amount of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within the specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements as constraints; constructing a constraint violation function through the target constraint conditions; constructing an initial batching model for the mixed materials; sequentially optimizing the initial batching model using the third generation non-dominated sorting genetic algorithm and the chaotic simulated annealing algorithm, and generating an optimized batching scheme for the mixed materials according to the output target batching model of the mixed materials. The present invention optimizes the model by combining the two algorithms, and improves the batching efficiency based on the optimized model under the condition of satisfying material compatibility.

Description

Technical Field

[0001] The invention relates to the technical field of alloy smelting, and in particular to a smelting and casting batching method and system based on mixed materials. Background Art

[0002] In the alloy smelting industry, the batching process is crucial and directly affects the quality of the final product and production efficiency. In actual use, a long production process often leaves behind a lot of scrapped products, scraps and other return materials, which results in a large amount of backlogged return materials. In order to facilitate transportation and storage, multiple return materials are often combined into one package for storage. In addition, due to various reasons, such as equipment limitations, operating habits or loading and unloading operations, and in order to avoid the tedious work of unpacking and picking up materials, the whole package of materials is often required to be put in at one time in the smelting batching process, which brings difficulties to the precise control of the proportion of each material. However, not all materials can be mixed arbitrarily. There may be chemical reactions or physical property conflicts between some materials. Improper matching may lead to instability in the smelting process and even endanger safe production.

[0003] In the prior art, the empirical formula method is generally used for the batching method in this industry. The empirical formula method describes the relationship between variables through a mathematical formula. The mathematical formula is usually obtained by fitting experimental data, and there is no complete theoretical derivation process. The calculation for a single ingredient is relatively simple, but for the batching of mixed materials, there are more variables involved, and the calculation is more complicated. The calculation process is relatively slow, which leads to the problem of low efficiency of the batching process. Summary of the invention

[0004] The embodiments of the present application provide a melting and casting batching method and system based on mixed materials, which are used to solve the problem of low efficiency in the melting and casting batching process.

[0005] The first aspect of the embodiment of the present application provides a melting and casting batching method based on mixed materials, comprising:

[0006] constructing an objective function based on the objective variables, the objective variables including the product quality of the mixed material, the number of storage areas for the marked mixed material, and the cost of the mixed material;

[0007] The constructing of the objective function based on the objective variable comprises:

[0008] The expression of the target variable as product quality is as follows:

[0009]

[0010] The expression for the target variable to be the number of marked storage areas is as follows:

[0011]

[0012] The expression for the target variable as material cost is as follows:

[0013]

[0014] Where: J is the number of controlled chemical elements of the target alloy product, j is the controlled chemical element index, is the target mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product; is the number of storage areas, is the bucket index, It is Hit mark of each storage area; is the amount of mixed material, is its index, For the The number of components of a mixed material, is its index, is the quantity of a single material, is its index, is the hit mark of the mth mixed material, is the mth mixed material The weight of the material, is the mth mixed material The unit cost of a material, is the qth single material input, It is Unit cost of a single material;

[0015] Determine target constraints according to the target function, wherein the target constraints include that the input of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within a specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements;

[0016] Determining the target constraint condition according to the target function includes:

[0017] The constraint that the input quantity of a single material cannot exceed the inventory quantity is expressed as follows:

[0018]

[0019] The constraint condition is that the mass content of the controlled chemical elements of the target product is within the specified range as follows:

[0020]

[0021] The constraint that there should be no conflict between materials is as follows:

[0022]

[0023]

[0024] The constraint condition is that the actual weight of the product meets the requirements. The expression is as follows:

[0025]

[0026] in: is the qth single material input, It is Inventory quantity of a single material; is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product; is the number of conflicts between materials, , is the mixed material index, It is The number of materials contained in a mixed material, It is The number of materials contained in a mixed material, It is Of the mixed materials Materials and Of the mixed materials Conflict indicators for each material, is the qth single material and the mth mixed material Conflict indicators for each material, It is A single material and conflicting marks of a single material; is the actual weight of the product, is the target weight of the product;

[0027] Constructing a constraint violation function through the target constraint condition;

[0028] The expression of the constraint violation function is as follows:

[0029]

[0030] in: is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual weight of the product, is the target weight of the product, is the number of conflicts between materials, is the quantity of a single material, is the amount of mixed material, For the The number of ingredients in a mixed material;

[0031] Constructing an initial batching model of the mixed material based on the objective function and the constraint violation function;

[0032] The initial batching model is optimized by using a third generation non-dominated sorting genetic algorithm and a chaotic simulated annealing algorithm in turn, and a target batching model of the mixed material is output;

[0033] An optimized batching scheme for the mixed material is generated according to the target batching model.

[0034] Furthermore, the constructing of an initial batching model of the mixed material based on the objective function and the constraint violation function includes:

[0035] The batching information parameters of the initial batching model are determined based on the objective function and the constraint violation function, and the initial batching scheme is determined according to the batching information parameters.

[0036] Furthermore, the optimization of the initial batching model by using the third generation non-dominated sorting genetic algorithm includes:

[0037] Using the parameters in the initial batching model as the initial population of the third generation non-dominated sorting genetic algorithm, calculating the target value and constraint violation degree of the initial batching model, and generating a reference point according to the target value;

[0038] The algorithm is iterated based on the reference point, and when the number of iterations reaches a preset maximum number, the iteration is stopped and the optimized first model is output.

[0039] Furthermore, the algorithm iteration is performed based on the reference point, and when the number of iterations reaches a preset maximum number, the iteration is stopped and the optimized first model is output, including:

[0040] When performing algorithm iterations, crossover and mutation calculations are performed on the current population in iteration to generate a descendant population;

[0041] Generating a new population based on merging the offspring population with its parent population, and performing non-dominated sorting on the new population according to the calculated target value and constraint violation degree of the new population;

[0042] According to the result of the non-dominated sorting, the new population is subjected to a selection operation based on the reference point to obtain a next generation population, and then the next iteration is performed until the number of iterations reaches a preset maximum number, and the optimized first model is output.

[0043] Furthermore, the optimization of the initial batching model using a chaotic simulated annealing algorithm includes:

[0044] Using the optimized first model as the initial population of the chaotic simulated annealing algorithm, setting parameter information, wherein the parameter information is an initial temperature and a termination temperature;

[0045] The algorithm is iterated based on the parameter information. If the current temperature is lower than the termination temperature, the iteration is stopped and the target batching model is output.

[0046] Furthermore, the algorithm iteration is performed based on the parameter information, and if the current temperature is lower than the termination temperature, the iteration is stopped and the target batching model is output, including:

[0047] Using a chaotic sequence to perturb each individual of the initial population to generate a new individual, and calculating the target value and constraint violation degree of the new individual;

[0048] Determining whether to accept the new individual according to the target value and constraint violation degree of the new individual;

[0049] The next iteration is performed according to the result of determining whether to accept the new individual until the current temperature is lower than the termination temperature and the target batching model is output.

[0050] A second aspect of the embodiment of the present application provides a melting and casting batching system based on mixed materials, comprising:

[0051] an objective function construction unit, configured to construct an objective function based on objective variables, wherein the objective variables include a product quality of the mixed material, a number of storage areas for the marked mixed material, and a cost of the mixed material;

[0052] The constructing of the objective function based on the objective variable comprises:

[0053] The expression of the target variable as product quality is as follows:

[0054]

[0055] The expression for the target variable to be the number of marked storage areas is as follows:

[0056]

[0057] The expression for the target variable as material cost is as follows:

[0058]

[0059] Where: J is the number of controlled chemical elements of the target alloy product, j is the controlled chemical element index, is the target mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product; is the number of storage areas, is the bucket index, It is Hit mark of each storage area; is the amount of mixed material, is its index, For the The number of components of a mixed material, is its index, is the quantity of a single material, is its index, is the hit mark of the mth mixed material, is the mth mixed material The weight of the material, is the mth mixed material The unit cost of a material, is the qth single material input, It is Unit cost of a single material;

[0060] A target constraint determination unit, used to determine the target constraint according to the target function, wherein the target constraint includes that the input of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within a specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements;

[0061] Determining the target constraint condition according to the target function includes:

[0062] The constraint that the input quantity of a single material cannot exceed the inventory quantity is expressed as follows:

[0063]

[0064] The constraint condition is that the mass content of the controlled chemical elements of the target product is within the specified range as follows:

[0065]

[0066] The constraint that there should be no conflict between materials is as follows:

[0067]

[0068]

[0069] The constraint condition is that the actual weight of the product meets the requirements. The expression is as follows:

[0070]

[0071] in: is the qth single material input, It is The inventory of a single material; is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product; is the number of conflicts between materials, , is the mixed material index, It is The number of materials contained in a mixed material, It is The number of materials contained in a mixed material, It is Of the mixed materials Materials and Of the mixed materials Conflict markers for each material, is the qth single material and the mth mixed material Conflict indicators for each material, It is A single material and conflicting marks of a single material; is the actual weight of the product, is the target weight of the product;

[0072] A constraint violation function construction unit, used to construct a constraint violation function according to the target constraint condition;

[0073] The expression of the constraint violation function is as follows:

[0074]

[0075] in: is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual weight of the product, is the target weight of the product, is the number of conflicts between materials, is the quantity of a single material, is the amount of mixed material, For the The number of ingredients in a mixed material;

[0076] An initial batching model building unit, used to build an initial batching model of the mixed material based on the objective function and the constraint violation function;

[0077] A target batching model output unit is used to optimize the initial batching model by using a third-generation non-dominated sorting genetic algorithm and a chaotic simulated annealing algorithm in sequence, and output a target batching model of the mixed material;

[0078] The optimized batching scheme generating unit is used to generate an optimized batching scheme of the mixed material according to the target batching model.

[0079] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0080] The present invention sets an optimization model for batching of mixed materials based on the alloy smelting and casting industry, and before determining the optimization model, an objective function is constructed based on the field, wherein the objective function variables include the product quality of the mixed material, the number of storage areas of the marked mixed material, and the cost of the mixed material, and then the target constraint conditions are set, and the target constraint conditions include that the input of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within the specified range, there can be no conflict between the materials, and the actual weight of the product meets the requirements; then the constraint violation function is constructed, and then the initial model is obtained, and then the model is optimized using the third generation non-dominated sorting genetic algorithm and the chaotic simulated annealing algorithm, and finally the batching optimization scheme is generated according to the optimized model. The present invention sets the objective function and constraint conditions of the initial model based on the parameters involved in the smelting and casting batching process, so that the model derivation process is relatively clear and complete, and then two algorithms are used based on the initial model for targeted solution, which can effectively improve the accuracy of the optimization scheme obtained based on the optimization model. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic flow chart of an embodiment of a melting and casting batching method based on mixed materials provided by the present invention;

[0082] Figure 2 A schematic flow chart of another embodiment of a melting and casting batching method based on mixed materials provided by the present invention;

[0083] Figure 3A schematic flow chart of another embodiment of a melting and casting batching method based on mixed materials provided by the present invention;

[0084] Figure 4 A structural block diagram of an embodiment of a melting and casting batching system based on mixed materials provided by the present invention. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] The melting and casting batching method based on mixed materials in this embodiment is used to improve the efficiency of melting and casting mixed batching. The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and is not specifically limited.

[0087] Embodiment 1

[0088] See also Figure 1 An embodiment of a melting and casting batching method based on mixed materials provided by the present invention comprises the following steps:

[0089] S11. constructing an objective function based on the target variables, the target variables including the product quality of the mixed material, the number of storage areas for the marked mixed material, and the cost of the mixed material;

[0090] In this embodiment, the determination of the target variable is based on the selection of parameters involved in the batching process of alloy casting, and the accurate parameters are obtained through a large number of experimental calculations, wherein the target variables include the product quality of the mixed material, the number of storage areas for the marked mixed material, and the cost of the mixed material. Based on the determined target variables, the corresponding target function can be determined. Specifically, the mixed material refers to a material package in which the number of materials is greater than or equal to 1, and the entire package of materials is required to be invested at one time.

[0091] The expression of the target variable as product quality is as follows:

[0092]

[0093] Where: J is the number of controlled chemical elements of the target alloy product, j is the controlled chemical element index, is the target mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, The expression is:

[0094]

[0095] Where: M is the number of mixed materials, m is the mixed material index, is the hit flag of the mth mixed material (i.e. whether it is selected for input), The expression is:

[0096]

[0097] is the weight of the mth mixed material, The expression is:

[0098]

[0099] in: is the number of materials contained in the mth mixed material, is the mth mixed material The weight of the material, is the mass content of the jth controlled chemical element of the mth mixed material, The expression is:

[0100]

[0101] in: is the mth mixed material The mass content of the jth controlled chemical element of a material, Q is the number of a single material, q is the index of a single material, is the qth single material input, is the mass content of the jth controlled chemical element of the qth single material.

[0102] is the actual weight of the product, The expression is:

[0103]

[0104] The expression for the target variable to be the number of marked storage areas is as follows:

[0105]

[0106] Among them: Among them, is the hit mark of the pth storage area (i.e., whether the storage area stores the hit mixed material or the single material selected for input), The expression is:

[0107]

[0108] in: is the number of hit materials contained in the pth storage area, The expression is:

[0109]

[0110]

[0111]

[0112] in: is the storage area where the mth mixed material is located, It is the storage area where the qth single material is located. By controlling the number of marked (hit) storage areas, the job scheduling cost and transportation cost are indirectly controlled.

[0113] The expression for the target variable as material cost is as follows:

[0114]

[0115] in: For the The number of components of a mixed material, is its index, is the quantity of a single material, is its index, is the hit mark of the mth mixed material, is the mth mixed material The weight of the material, is the mth mixed material The unit cost of a material, is the qth single material input, It is The unit cost of a single material.

[0116] S12. Determine the target constraints according to the target function. The target constraints include that the input of a single material cannot exceed the inventory, the mass content of the controlled chemical elements of the target product is within the specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements;

[0117] Based on different fields, the selection of constraints of the objective function before building the model is different. In this embodiment, the constraints include that the input amount of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within the specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements. Specifically, a single material means that the number of material types in a material package is equal to 1, and the entire package of materials is not required to be put in at one time, and the amount can be finely controlled and the precise amount can be put in according to actual needs. The target product refers to an item formed by melting multiple mixed materials and multiple single materials together.

[0118] Specifically, the constraint that the input quantity of a single material cannot exceed the inventory quantity is expressed as follows:

[0119]

[0120] in, is the qth single material input, It is The inventory of a single material.

[0121] The constraint condition is that the mass content of the controlled chemical elements of the target product is within the specified range as follows:

[0122]

[0123] in: is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, It is the maximum allowable mass content of the jth controlled chemical element in the target alloy product.

[0124] The constraint that there should be no conflict between materials is as follows:

[0125]

[0126]

[0127] in: , is the mixed material index, It is The number of materials contained in a mixed material, It is The number of materials contained in a mixed material, It is Of the mixed materials Materials and Of the mixed materials Conflict indicators for each material, The expression is:

[0128]

[0129] When 1 is and And the first Of the mixed materials Materials and Of the mixed materials The materials are incompatible, and 0 indicates the opposite.

[0130] is the qth single material and the mth mixed material The conflict flag of a material is expressed as:

[0131]

[0132] When 1 is and And the first A single material and Of the mixed materials The materials are incompatible, and 0 indicates the opposite.

[0133] It is A single material and A single material , the expression is:

[0134]

[0135] When 1 is and And the first A single material and A single material is incompatible, and 0 indicates the opposite.

[0136] The constraint condition is that the actual weight of the product meets the requirements. The expression is as follows:

[0137]

[0138] in: is the actual weight of the product, is the target weight of the product.

[0139] S13. construct a constraint violation function through the target constraint condition;

[0140] The expression of the constraint violation function constructed according to the target constraint conditions determined in the above steps is as follows:

[0141]

[0142] in: is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual weight of the product, is the target weight of the product, is the number of conflicts between materials, is the quantity of a single material, is the amount of mixed material, For the The number of ingredients in a mixed material.

[0143] S14. constructing an initial batching model of the mixed material based on the objective function and the constraint violation function;

[0144] In this embodiment, an initial batching model of mixed materials is constructed based on the objective function and the constraint violation function, including:

[0145] The batching information parameters of the initial batching model are determined based on the objective function and the constraint violation function, and the initial batching scheme is determined according to the batching information parameters.

[0146] Specifically, the ingredient information parameters include:

[0147] The number of single materials is Q, the number of mixed materials is M, and the number of controlled chemical elements in the product is J. The number of constituent materials of each mixed material , the mth mixed material Weight of the material , Unit Cost , the mass content of the jth (j=1,2,...J) controlled chemical element . No. Unit cost of a single material and the mass content of the jth controlled chemical element . Product target weight , target mass content of the jth controlled chemical element , the minimum value allowed , the maximum value allowed , where j = 1, 2, ... J. The storage area where the mth mixed material is located , the storage area where the qth single material is located .

[0148] According to the above determined parameter information, randomly generate An initial batching scheme, that is, an initial batching model, is expressed as:

[0149] };

[0150] in, Indicates The material markings for all mixed materials in a batching scheme, Indicates Whether the first mixed material in the batching scheme is put into use, Indicates Whether the second mixed material in the batching scheme is put in, and so on; Indicates The amount of each single material in a batching scheme, Indicates The first single material input in a batching scheme, Indicates The input amount of the second single material in the first batching scheme, and so on.

[0151] S15. Optimizing the initial batching model using the third generation non-dominated sorting genetic algorithm and the chaotic simulated annealing algorithm in turn, and outputting the target batching model of the mixed material;

[0152] After the initial batching model is determined as above, the initial batching model is optimized by using the third generation non-dominated sorting genetic algorithm with constraint priority and the chaotic simulated annealing algorithm. Both algorithms involve the target value and constraint violation degree of the initial batching model, and the optimization model is determined based on the target value and constraint violation degree.

[0153] S16. Generate an optimized batching scheme for the mixed material according to the target batching model.

[0154] Finally, the optimal solution for mixing the mixed materials is output according to the optimized batching model, and the staff can batch the materials according to the optimized solution. The present invention uses two improved algorithms to realize computer rapid automatic calculation based on the target value and constraint violation degree of the calculation model, greatly improves the efficiency of alloy smelting batching, and improves the accuracy of the model to a certain extent.

[0155] Embodiment 2

[0156] See also Figure 2 In the present invention, the optimization of the initial batching model using the third generation non-dominated sorting genetic algorithm includes the following steps:

[0157] S151. Using the parameters in the initial batching model as the initial population of the third-generation non-dominated sorting genetic algorithm, calculating the target value and constraint violation degree of the initial batching model, and generating a reference point according to the target value;

[0158] S152. When performing algorithm iteration, performing crossover and mutation calculations on the current population in iteration to generate a descendant population;

[0159] S153. Generate a new population based on the merging of the offspring population and its parent population, and perform non-dominated sorting on the new population according to the calculated target value and constraint violation degree of the new population;

[0160] S154. Perform a reference point selection operation on the new population according to the result of the non-dominated sorting to obtain the next generation population, and then perform the next iteration until the number of iterations reaches a preset maximum number, and output the optimized first model.

[0161] Specifically, the initial batching model is used as the initial population of the algorithm, and the target value and constraint violation degree of each individual in the initial population are calculated according to the objective function and constraint violation degree function, and the reference point is generated according to the target values ​​of all individuals in the initial population. The algorithm parameters include the maximum number of iterations , crossover probability , mutation probability ; If the current number of iterations reaches the preset maximum number , then stop the iteration and output the optimized batching scheme. Otherwise, perform crossover and mutation operations on the current population to generate a child population, calculate the target value and constraint violation degree of each individual in the child population, merge the child population with the parent population to generate a new population, and then perform non-dominated sorting on the new population based on the calculated target value and constraint violation degree of the new population.

[0162] The non-dominated sorting is to use the constraint priority dominance rule to sort the population non-dominatedly, where the constraint priority dominance rule is: if the constraint violation degree of individual A is less than that of individual B, then individual A dominates individual B; otherwise, if the constraint violation degrees of individual A and individual B are equal and each target value of individual A is less than the corresponding target value of individual B, then individual A dominates individual B. Finally, according to the result of the non-dominated sorting, the population is selected based on the reference point, and L individuals are selected as the next generation population and iterated until the number of iterations reaches the preset maximum number, and the optimized model is output.

[0163] In this embodiment, a constraint priority condition is added on the basis of the traditional algorithm, so that the obtained optimization model has higher stability and accuracy.

[0164] Embodiment 3

[0165] See also Figure 3 In the present invention, the optimization of the initial batching model using the chaotic simulated annealing algorithm includes the following steps:

[0166] S155. Using the optimized first model as the initial population of the chaotic simulated annealing algorithm, setting parameter information, the parameter information being the initial temperature and the termination temperature;

[0167] S156. Use the chaotic sequence to perturb each individual of the initial population to generate a new individual, and calculate the target value and constraint violation degree of the new individual;

[0168] S157. Determine whether to accept the new individual based on the target value and constraint violation of the new individual;

[0169] S158. Perform the next iteration based on the result of determining whether to accept the new individual until the previous temperature is lower than the termination temperature and output the target batching model.

[0170] Specifically, the optimization model output in the above-mentioned embodiment 3 is used as the initial population of the chaotic simulated annealing algorithm, and the initial temperature and the termination temperature are set. If the current temperature is lower than the termination temperature, the iteration is stopped, and the optimized final batching scheme, that is, the target batching model, is output. Otherwise, each individual in the population is disturbed by a chaotic sequence to generate a new individual, and the objective function value and constraint violation degree of the new individual are calculated. If the constraint violation degree of the new individual is less than the constraint violation degree of the original individual or the constraint violation degree of the new individual is equal to the constraint violation degree of the original individual and each objective function value of the new individual is smaller than the corresponding objective function value of the original individual, the new individual is accepted. Otherwise, the Metropolis criterion is used to decide whether to accept the new individual and perform a cooling process. Then, the next iteration is performed according to the result of whether to accept the new individual until the previous temperature is lower than the termination temperature, and the final optimized batching scheme is output.

[0171] The present invention optimizes the model based on the above two algorithms, so that the model has strong adaptability and good convergence characteristics, effectively avoiding the local optimal problem while improving the local search accuracy.

[0172] Embodiment 4

[0173] See also Figure 4 An embodiment of the molten-casting batching system based on mixed materials of the present invention comprises the following steps:

[0174] An objective function construction unit 101 is used to construct an objective function based on objective variables, where the objective variables include product quality of mixed materials, the number of storage areas for marked mixed materials, and the cost of mixed materials;

[0175] The target constraint determination unit 102 is used to determine the target constraint according to the target function, wherein the target constraint includes that the input of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within a specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements;

[0176] A constraint violation function construction unit 103, used to construct a constraint violation function through target constraint conditions;

[0177] An initial batching model building unit 104 is used to build an initial batching model of the mixed material based on the objective function and the constraint violation function;

[0178] The target batching model output unit 105 is used to optimize the initial batching model by using the third generation non-dominated sorting genetic algorithm and the chaotic simulated annealing algorithm in sequence, and output the target batching model of the mixed material;

[0179] The optimized batching scheme generating unit 106 is used to generate an optimized batching scheme of the mixed material according to the target batching model.

[0180] The specific definition of the batching system mentioned above can be found in the definition of the batching method mentioned above, which will not be repeated here. Each unit in the above batching system can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0181] It is understandable that those skilled in the art can, under the guidance of the above embodiments, combine various implementation methods in the above embodiments to obtain technical solutions of multiple implementation methods.

[0182] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A melting and casting batching method based on mixed materials, characterized in that: include: constructing an objective function based on the objective variables, the objective variables including the product quality of the mixed material, the number of storage areas for the marked mixed material, and the cost of the mixed material; The constructing of the objective function based on the objective variable comprises: The expression of the target variable as product quality is as follows: The expression for the target variable to be the number of marked storage areas is as follows: The expression for the target variable as material cost is as follows: Where: J is the number of controlled chemical elements of the target alloy product, j is the controlled chemical element index, is the target mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product; is the number of storage areas, is the bucket index, It is Hit mark of each storage area; is the amount of mixed material, is its index, For the The number of components of a mixed material, is its index, is the quantity of a single material, is its index, is the hit mark of the mth mixed material, is the mth mixed material The weight of the material, is the mth mixed material The unit cost of a material, is the qth single material input, It is Unit cost of a single material; Determine target constraints according to the target function, wherein the target constraints include that the input of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within a specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements; Determining the target constraint condition according to the target function includes: The constraint that the input quantity of a single material cannot exceed the inventory quantity is expressed as follows: The constraint condition is that the mass content of the controlled chemical elements of the target product is within the specified range as follows: The constraint that there should be no conflict between materials is as follows: The constraint condition is that the actual weight of the product meets the requirements. The expression is as follows: in: is the qth single material input, It is Inventory quantity of a single material; is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product; is the number of conflicts between materials, , is the mixed material index, It is The number of materials contained in a mixed material, It is The number of materials contained in a mixed material, It is Of the mixed materials Materials and Of the mixed materials Conflict indicators for each material, is the qth single material and the mth mixed material Conflict indicators for each material, It is A single material and conflicting marks of a single material; is the actual weight of the product, is the target weight of the product; Constructing a constraint violation function through the target constraint condition; The expression of the constraint violation function is as follows: in: is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual weight of the product, is the target weight of the product, is the number of conflicts between materials, is the quantity of a single material, is the amount of mixed material, For the The number of ingredients in a mixed material; Constructing an initial batching model of the mixed material based on the objective function and the constraint violation function; The initial batching model is optimized by using a third generation non-dominated sorting genetic algorithm and a chaotic simulated annealing algorithm in turn, and a target batching model of the mixed material is output; An optimized batching scheme for the mixed material is generated according to the target batching model.

2. The melting and casting batching method based on mixed materials according to claim 1, characterized in that: The initial batching model of the mixed material is constructed based on the objective function and the constraint violation function, including: The batching information parameters of the initial batching model are determined based on the objective function and the constraint violation function, and the initial batching scheme is determined according to the batching information parameters.

3. The melting and casting batching method based on mixed materials according to claim 1, characterized in that: The method of optimizing the initial batching model by using the third generation non-dominated sorting genetic algorithm comprises: Using the parameters in the initial batching model as the initial population of the third generation non-dominated sorting genetic algorithm, calculating the target value and constraint violation degree of the initial batching model, and generating a reference point according to the target value; The algorithm is iterated based on the reference point, and when the number of iterations reaches a preset maximum number, the iteration is stopped and the optimized first model is output.

4. The melting and casting batching method based on mixed materials according to claim 3 is characterized in that: The algorithm iteration is performed based on the reference point, and when the number of iterations reaches a preset maximum number, the iteration is stopped and the optimized first model is output, including: When performing algorithm iterations, crossover and mutation calculations are performed on the current population in iteration to generate a descendant population; Generating a new population based on merging the offspring population with its parent population, and performing non-dominated sorting on the new population according to the calculated target value and constraint violation degree of the new population; According to the result of the non-dominated sorting, the new population is subjected to a selection operation based on the reference point to obtain a next generation population, and then the next iteration is performed until the number of iterations reaches a preset maximum number, and the optimized first model is output.

5. The melting and casting batching method based on mixed materials according to claim 4, characterized in that: The method of optimizing the initial batching model by using a chaotic simulated annealing algorithm comprises: Using the optimized first model as the initial population of the chaotic simulated annealing algorithm, setting parameter information, wherein the parameter information is an initial temperature and a termination temperature; The algorithm is iterated based on the parameter information. If the current temperature is lower than the termination temperature, the iteration is stopped and the target batching model is output.

6. The melting and casting batching method based on mixed materials according to claim 5, characterized in that: The algorithm iteration is performed based on the parameter information, and if the current temperature is lower than the termination temperature, the iteration is stopped and the target batching model is output, including: Using a chaotic sequence to perturb each individual of the initial population to generate a new individual, and calculating the target value and constraint violation degree of the new individual; Determining whether to accept the new individual according to the target value and constraint violation degree of the new individual; The next iteration is performed according to the result of determining whether to accept the new individual until the current temperature is lower than the termination temperature and the target batching model is output.

7. A melting and casting batching system based on mixed materials, characterized in that: include: an objective function construction unit, configured to construct an objective function based on objective variables, wherein the objective variables include a product quality of the mixed material, a number of storage areas for the marked mixed material, and a cost of the mixed material; The constructing of the objective function based on the objective variable comprises: The expression of the target variable as product quality is as follows: The expression for the target variable to be the number of marked storage areas is as follows: The expression for the target variable as material cost is as follows: Where: J is the number of controlled chemical elements of the target alloy product, j is the controlled chemical element index, is the target mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product; is the number of storage areas, is the bucket index, It is Hit mark of each storage area; is the amount of mixed material, is its index, For the The number of components of a mixed material, is its index, is the quantity of a single material, is its index, is the hit mark of the mth mixed material, is the mth mixed material The weight of the material, is the mth mixed material The unit cost of a material, is the qth single material input, It is Unit cost of a single material; A target constraint determination unit, used to determine the target constraint according to the target function, wherein the target constraint includes that the input of a single material cannot exceed the inventory, the controlled chemical element mass content of the target product is within a specified range, there can be no conflict between materials, and the actual weight of the product meets the requirements; Determining the target constraint condition according to the target function includes: The constraint that the input quantity of a single material cannot exceed the inventory quantity is expressed as follows: The constraint condition is that the mass content of the controlled chemical elements of the target product is within the specified range as follows: The constraint that there should be no conflict between materials is as follows: The constraint condition is that the actual weight of the product meets the requirements. The expression is as follows: in: is the qth single material input, It is Inventory quantity of a single material; is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product; is the number of conflicts between materials, , is the mixed material index, It is The number of materials contained in a mixed material, It is The number of materials contained in a mixed material, It is Of the mixed materials Materials and Of the mixed materials Conflict indicators for each material, is the qth single material and the mth mixed material Conflict indicators for each material, It is A single material and conflicting marks of a single material; is the actual weight of the product, is the target weight of the product; A constraint violation function construction unit, used to construct a constraint violation function according to the target constraint condition; The expression of the constraint violation function is as follows: in: is the minimum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual mass content of the jth controlled chemical element in the target alloy product, is the maximum value allowed for the mass content of the jth controlled chemical element in the target alloy product, is the actual weight of the product, is the target weight of the product, is the number of conflicts between materials, is the quantity of a single material, is the amount of mixed material, For the The number of ingredients in a mixed material; An initial batching model building unit, used to build an initial batching model of the mixed material based on the objective function and the constraint violation function; A target batching model output unit is used to optimize the initial batching model by using a third-generation non-dominated sorting genetic algorithm and a chaotic simulated annealing algorithm in sequence, and output a target batching model of the mixed material; The optimized batching scheme generating unit is used to generate an optimized batching scheme of the mixed material according to the target batching model.

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